{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b69b2748-8379-435e-aa1e-955c8d88ecfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pymysql in /opt/conda/lib/python3.11/site-packages (1.1.1)\n",
      "✅ pymysql 安装成功\n",
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.11/site-packages (2.3.0)\n",
      "Requirement already satisfied: numpy>=1.23.2 in /opt/conda/lib/python3.11/site-packages (from pandas) (2.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.11/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.11/site-packages (from pandas) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.11/site-packages (from pandas) (2025.2)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "✅ pandas 安装成功\n",
      "Requirement already satisfied: sqlalchemy in /opt/conda/lib/python3.11/site-packages (2.0.22)\n",
      "Requirement already satisfied: typing-extensions>=4.2.0 in /opt/conda/lib/python3.11/site-packages (from sqlalchemy) (4.8.0)\n",
      "Requirement already satisfied: greenlet!=0.4.17 in /opt/conda/lib/python3.11/site-packages (from sqlalchemy) (3.0.0)\n",
      "✅ sqlalchemy 安装成功\n",
      "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.11/site-packages (3.10.3)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.11/site-packages (from matplotlib) (1.3.2)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.11/site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.11/site-packages (from matplotlib) (4.58.4)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.11/site-packages (from matplotlib) (1.4.8)\n",
      "Requirement already satisfied: numpy>=1.23 in /opt/conda/lib/python3.11/site-packages (from matplotlib) (2.3.1)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.11/site-packages (from matplotlib) (23.2)\n",
      "Requirement already satisfied: pillow>=8 in /opt/conda/lib/python3.11/site-packages (from matplotlib) (11.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.11/site-packages (from matplotlib) (3.2.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.11/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "✅ matplotlib 安装成功\n",
      "Requirement already satisfied: seaborn in /opt/conda/lib/python3.11/site-packages (0.13.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /opt/conda/lib/python3.11/site-packages (from seaborn) (2.3.1)\n",
      "Requirement already satisfied: pandas>=1.2 in /opt/conda/lib/python3.11/site-packages (from seaborn) (2.3.0)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /opt/conda/lib/python3.11/site-packages (from seaborn) (3.10.3)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.2)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.58.4)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.8)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.2)\n",
      "Requirement already satisfied: pillow>=8 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (11.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
      "✅ seaborn 安装成功\n",
      "\n",
      "🎉 所有依赖安装完成！\n"
     ]
    }
   ],
   "source": [
    "# 安装必要的依赖包\n",
    "import subprocess\n",
    "import sys\n",
    "\n",
    "def install_package(package):\n",
    "    \"\"\"安装 Python 包\"\"\"\n",
    "    try:\n",
    "        subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", package])\n",
    "        print(f\"✅ {package} 安装成功\")\n",
    "    except subprocess.CalledProcessError:\n",
    "        print(f\"❌ {package} 安装失败\")\n",
    "\n",
    "# 安装连接 Doris 所需的依赖\n",
    "packages = ['pymysql', 'pandas', 'sqlalchemy', 'matplotlib', 'seaborn']\n",
    "for package in packages:\n",
    "    install_package(package)\n",
    "\n",
    "print(\"\\n🎉 所有依赖安装完成！\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7228343",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 数据库连接配置:\n",
      "主机: host.docker.internal\n",
      "端口: 9030\n",
      "用户: root\n",
      "密码: ****\n"
     ]
    }
   ],
   "source": [
    "# 导入必要的库\n",
    "import pandas as pd\n",
    "import pymysql\n",
    "from sqlalchemy import create_engine\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 数据库连接配置\n",
    "DB_CONFIG = {\n",
    "    'host': 'host.docker.internal',\n",
    "    'port': 9030,\n",
    "    'user': 'root',\n",
    "    'password': 'pass',\n",
    "    'charset': 'utf8mb4'\n",
    "}\n",
    "\n",
    "print(\"📊 数据库连接配置:\")\n",
    "print(f\"主机: {DB_CONFIG['host']}\")\n",
    "print(f\"端口: {DB_CONFIG['port']}\")\n",
    "print(f\"用户: {DB_CONFIG['user']}\")\n",
    "print(f\"密码: {'*' * len(DB_CONFIG['password'])}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f5c5c148",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 数据库连接测试成功！\n",
      "📋 数据库版本: 5.7.99\n",
      "📁 可用数据库: ['__internal_schema', 'demo', 'demo_6', 'information_schema', 'mysql']\n"
     ]
    }
   ],
   "source": [
    "# 测试数据库连接\n",
    "def test_connection():\n",
    "    \"\"\"测试与 Doris 数据库的连接\"\"\"\n",
    "    try:\n",
    "        # 使用 pymysql 测试连接\n",
    "        connection = pymysql.connect(**DB_CONFIG)\n",
    "        print(\"✅ 数据库连接测试成功！\")\n",
    "        \n",
    "        # 获取数据库版本信息\n",
    "        with connection.cursor() as cursor:\n",
    "            cursor.execute(\"SELECT VERSION();\")\n",
    "            version = cursor.fetchone()\n",
    "            print(f\"📋 数据库版本: {version[0]}\")\n",
    "            \n",
    "            # 显示当前数据库列表\n",
    "            cursor.execute(\"SHOW DATABASES;\")\n",
    "            databases = cursor.fetchall()\n",
    "            print(f\"📁 可用数据库: {[db[0] for db in databases]}\")\n",
    "        \n",
    "        connection.close()\n",
    "        return True\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"❌ 数据库连接失败: {str(e)}\")\n",
    "        return False\n",
    "\n",
    "# 执行连接测试\n",
    "connection_success = test_connection()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "90633c56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ SQLAlchemy 引擎创建成功！\n",
      "\n",
      "🔗 现在可以使用 pandas 进行数据库操作了！\n"
     ]
    }
   ],
   "source": [
    "# 创建 SQLAlchemy 引擎用于 pandas 操作\n",
    "def create_engine_connection():\n",
    "    \"\"\"创建 SQLAlchemy 引擎\"\"\"\n",
    "    try:\n",
    "        # 构建连接字符串\n",
    "        connection_string = f\"mysql+pymysql://{DB_CONFIG['user']}:{DB_CONFIG['password']}@{DB_CONFIG['host']}:{DB_CONFIG['port']}\"\n",
    "        \n",
    "        # 创建引擎\n",
    "        engine = create_engine(connection_string, echo=False)\n",
    "        print(\"✅ SQLAlchemy 引擎创建成功！\")\n",
    "        return engine\n",
    "    except Exception as e:\n",
    "        print(f\"❌ SQLAlchemy 引擎创建失败: {str(e)}\")\n",
    "        return None\n",
    "\n",
    "# 创建数据库引擎\n",
    "engine = create_engine_connection()\n",
    "\n",
    "if engine:\n",
    "    print(\"\\n🔗 现在可以使用 pandas 进行数据库操作了！\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "29d128a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 执行省份统计查询...\n",
      "✅ 查询执行成功！\n",
      "📋 省份统计结果 (共 44 个省份):\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>province</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3839</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>533</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1243</td>\n",
       "      <td>甘肃省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>294</td>\n",
       "      <td>重庆</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2080</td>\n",
       "      <td>广西</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3353</td>\n",
       "      <td>河南省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4683</td>\n",
       "      <td>香港</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2757</td>\n",
       "      <td>四川省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>401</td>\n",
       "      <td>西藏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1385</td>\n",
       "      <td>河北省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2533</td>\n",
       "      <td>台湾省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>435</td>\n",
       "      <td>天津</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1937</td>\n",
       "      <td>新疆</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>吉林</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1671</td>\n",
       "      <td>黑龙江省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>761</td>\n",
       "      <td>贵州省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>455</td>\n",
       "      <td>海南省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>安徽</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>广东</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>山西</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2458</td>\n",
       "      <td>福建省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1789</td>\n",
       "      <td>安徽省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1837</td>\n",
       "      <td>江西省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2</td>\n",
       "      <td>江苏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6192</td>\n",
       "      <td>广东省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2893</td>\n",
       "      <td>江苏省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1111</td>\n",
       "      <td>山西省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1797</td>\n",
       "      <td>陕西省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2159</td>\n",
       "      <td>浙江省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1920</td>\n",
       "      <td>湖北省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2713</td>\n",
       "      <td>山东省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1670</td>\n",
       "      <td>湖南省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2</td>\n",
       "      <td>河北</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>贵州</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>512</td>\n",
       "      <td>青海省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>4</td>\n",
       "      <td>河南</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1779</td>\n",
       "      <td>内蒙古</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1640</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>46</td>\n",
       "      <td>澳门</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1979</td>\n",
       "      <td>辽宁省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1025</td>\n",
       "      <td>云南省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>957</td>\n",
       "      <td>吉林省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>696</td>\n",
       "      <td>宁夏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5</td>\n",
       "      <td>辽宁</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    count province\n",
       "0    3839       北京\n",
       "1     533        0\n",
       "2    1243      甘肃省\n",
       "3     294       重庆\n",
       "4    2080       广西\n",
       "5    3353      河南省\n",
       "6    4683       香港\n",
       "7    2757      四川省\n",
       "8     401       西藏\n",
       "9    1385      河北省\n",
       "10   2533      台湾省\n",
       "11    435       天津\n",
       "12   1937       新疆\n",
       "13      1       吉林\n",
       "14   1671     黑龙江省\n",
       "15    761      贵州省\n",
       "16    455      海南省\n",
       "17      1       安徽\n",
       "18      1       广东\n",
       "19      1       山西\n",
       "20   2458      福建省\n",
       "21   1789      安徽省\n",
       "22   1837      江西省\n",
       "23      2       江苏\n",
       "24   6192      广东省\n",
       "25   2893      江苏省\n",
       "26   1111      山西省\n",
       "27   1797      陕西省\n",
       "28   2159      浙江省\n",
       "29   1920      湖北省\n",
       "30   2713      山东省\n",
       "31   1670      湖南省\n",
       "32      2       河北\n",
       "33      1       贵州\n",
       "34    512      青海省\n",
       "35      4       河南\n",
       "36   1779      内蒙古\n",
       "37   1640       上海\n",
       "38     46       澳门\n",
       "39   1979      辽宁省\n",
       "40   1025      云南省\n",
       "41    957      吉林省\n",
       "42    696       宁夏\n",
       "43      5       辽宁"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🏆 IP地址数量前10的省份:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>province</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6192</td>\n",
       "      <td>广东省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4683</td>\n",
       "      <td>香港</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3839</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3353</td>\n",
       "      <td>河南省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2893</td>\n",
       "      <td>江苏省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2757</td>\n",
       "      <td>四川省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2713</td>\n",
       "      <td>山东省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2533</td>\n",
       "      <td>台湾省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2458</td>\n",
       "      <td>福建省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2159</td>\n",
       "      <td>浙江省</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    count province\n",
       "24   6192      广东省\n",
       "6    4683       香港\n",
       "0    3839       北京\n",
       "5    3353      河南省\n",
       "25   2893      江苏省\n",
       "7    2757      四川省\n",
       "30   2713      山东省\n",
       "10   2533      台湾省\n",
       "20   2458      福建省\n",
       "28   2159      浙江省"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📈 统计信息:\n",
      "• 总省份数: 44\n",
      "• 总IP地址数: 63,551\n",
      "• 平均每省IP数: 1444\n",
      "• 最大值: 6,192 (广东省)\n",
      "• 最小值: 1 (吉林)\n"
     ]
    }
   ],
   "source": [
    "# 导入必要的库\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from IPython.display import display\n",
    "\n",
    "# 执行省份统计查询\n",
    "def execute_province_count_query():\n",
    "    \"\"\"执行省份统计查询\"\"\"\n",
    "    try:\n",
    "        # 创建数据库连接字符串\n",
    "        connection_string = f\"mysql+pymysql://{DB_CONFIG['user']}:{DB_CONFIG['password']}@{DB_CONFIG['host']}:{DB_CONFIG['port']}/demo\"\n",
    "        query_engine = create_engine(connection_string, echo=False)\n",
    "        \n",
    "        # 执行指定的查询\n",
    "        query = \"\"\"\n",
    "        select count(*) as count, province from ip_geo ig\n",
    "        group by province;\n",
    "        \"\"\"\n",
    "        \n",
    "        # 使用 pandas 执行查询\n",
    "        result_df = pd.read_sql(query, query_engine)\n",
    "        \n",
    "        # 设置 pandas 显示选项\n",
    "        pd.set_option('display.max_columns', None)\n",
    "        pd.set_option('display.width', None)\n",
    "        pd.set_option('display.max_colwidth', 50)\n",
    "        pd.set_option('display.precision', 2)\n",
    "        \n",
    "        return result_df\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"❌ 查询执行失败: {str(e)}\")\n",
    "        return None\n",
    "\n",
    "# 执行查询并显示结果\n",
    "print(\"📊 执行省份统计查询...\")\n",
    "province_count_result = execute_province_count_query()\n",
    "\n",
    "# 显示查询结果\n",
    "if province_count_result is not None and not province_count_result.empty:\n",
    "    print(\"✅ 查询执行成功！\")\n",
    "    print(f\"📋 省份统计结果 (共 {len(province_count_result)} 个省份):\")\n",
    "    display(province_count_result)\n",
    "    \n",
    "    # 按计数排序显示前10名\n",
    "    top_provinces = province_count_result.sort_values('count', ascending=False).head(10)\n",
    "    print(f\"\\n🏆 IP地址数量前10的省份:\")\n",
    "    display(top_provinces)\n",
    "    \n",
    "    print(f\"\\n📈 统计信息:\")\n",
    "    print(f\"• 总省份数: {len(province_count_result)}\")\n",
    "    print(f\"• 总IP地址数: {province_count_result['count'].sum():,}\")\n",
    "    print(f\"• 平均每省IP数: {province_count_result['count'].mean():.0f}\")\n",
    "    print(f\"• 最大值: {province_count_result['count'].max():,} ({province_count_result.loc[province_count_result['count'].idxmax(), 'province']})\")\n",
    "    print(f\"• 最小值: {province_count_result['count'].min():,} ({province_count_result.loc[province_count_result['count'].idxmin(), 'province']})\")\n",
    "else:\n",
    "    print(\"⚠️ 查询没有返回任何数据\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b2daebe5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔧 正在配置中文字体...\n",
      "✅ 尝试安装系统中文字体\n",
      "⚠️ 字体配置警告: module 'matplotlib.font_manager' has no attribute '_rebuild'\n",
      "🎨 创建省份IP地址统计柱状图...\n",
      "⚠️ 检测到中文字体可能显示异常，将使用英文标签\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Chart Description:\n",
      "• Top-left: Top 15 provinces with most IP addresses\n",
      "• Top-right: Bottom 15 provinces with least IP addresses\n",
      "• Bottom-left: Overall distribution of IP counts, red line=mean, green line=median\n",
      "• Bottom-right: Percentage analysis of top 10 provinces\n",
      "\n",
      "🔍 Key Insights:\n",
      "• Top 5 provinces account for 33.0% of total IP addresses\n",
      "• Province with most IPs: 广东省 (6,192 IPs)\n",
      "• Province with least IPs: 贵州 (1 IPs)\n",
      "• IP count difference ratio: 6192x\n"
     ]
    }
   ],
   "source": [
    "# 设置字体支持（解决中文显示问题）\n",
    "import matplotlib.font_manager as fm\n",
    "\n",
    "# 检查可用字体并设置最佳中文字体\n",
    "def setup_chinese_fonts():\n",
    "    \"\"\"设置中文字体支持\"\"\"\n",
    "    # 获取系统可用字体\n",
    "    available_fonts = [f.name for f in fm.fontManager.ttflist]\n",
    "    \n",
    "    # 中文字体优先级列表\n",
    "    chinese_fonts = [\n",
    "        'SimHei',           # 黑体\n",
    "        'Microsoft YaHei',  # 微软雅黑\n",
    "        'Arial Unicode MS', # Arial Unicode MS\n",
    "        'Noto Sans CJK SC', # Noto Sans\n",
    "        'WenQuanYi Micro Hei',  # 文泉驿微米黑\n",
    "        'DejaVu Sans'       # DejaVu Sans (备选)\n",
    "    ]\n",
    "    \n",
    "    # 找到第一个可用的中文字体\n",
    "    selected_font = 'DejaVu Sans'  # 默认字体\n",
    "    for font in chinese_fonts:\n",
    "        if font in available_fonts:\n",
    "            selected_font = font\n",
    "            break\n",
    "    \n",
    "    print(f\"🔤 选择字体: {selected_font}\")\n",
    "    \n",
    "    # 设置matplotlib字体\n",
    "    plt.rcParams['font.sans-serif'] = [selected_font, 'DejaVu Sans']\n",
    "    plt.rcParams['axes.unicode_minus'] = False\n",
    "    plt.rcParams['font.size'] = 10\n",
    "    \n",
    "    return selected_font\n",
    "\n",
    "# 安装和配置中文字体\n",
    "try:\n",
    "    # 首先尝试安装字体包\n",
    "    import subprocess\n",
    "    import sys\n",
    "    \n",
    "    print(\"🔧 正在配置中文字体...\")\n",
    "    \n",
    "    # 尝试安装字体包（适用于Ubuntu/Debian系统）\n",
    "    try:\n",
    "        subprocess.run(['apt-get', 'update'], capture_output=True, check=False)\n",
    "        subprocess.run(['apt-get', 'install', '-y', 'fonts-wqy-microhei', 'fonts-wqy-zenhei'], \n",
    "                      capture_output=True, check=False)\n",
    "        print(\"✅ 尝试安装系统中文字体\")\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "    # 清除matplotlib字体缓存\n",
    "    try:\n",
    "        import shutil\n",
    "        import os\n",
    "        cache_dir = fm.get_cachedir()\n",
    "        if os.path.exists(cache_dir):\n",
    "            shutil.rmtree(cache_dir)\n",
    "        print(\"🧹 清除字体缓存\")\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "    # 重新构建字体列表\n",
    "    fm._rebuild()\n",
    "    \n",
    "    # 设置字体\n",
    "    selected_font = setup_chinese_fonts()\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠️ 字体配置警告: {e}\")\n",
    "    # 使用备选方案\n",
    "    plt.rcParams['font.sans-serif'] = ['DejaVu Sans']\n",
    "    plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 创建省份IP地址统计柱状图\n",
    "def create_province_count_charts():\n",
    "    \"\"\"基于省份统计查询结果创建柱状图\"\"\"\n",
    "    if 'province_count_result' not in globals() or province_count_result is None or province_count_result.empty:\n",
    "        print(\"⚠️ 没有省份统计数据可供可视化\")\n",
    "        return\n",
    "    \n",
    "    # 设置图表样式\n",
    "    plt.style.use('default')\n",
    "    sns.set_palette(\"viridis\")\n",
    "    \n",
    "    # 检测中文字体是否正常工作\n",
    "    test_fig, test_ax = plt.subplots(figsize=(1, 1))\n",
    "    test_ax.text(0.5, 0.5, '测试中文', fontsize=12)\n",
    "    test_fig.canvas.draw()\n",
    "    \n",
    "    # 检查是否显示为方框（简单检测）\n",
    "    use_english = False\n",
    "    try:\n",
    "        # 如果字体是DejaVu Sans，很可能中文会显示异常\n",
    "        current_font = plt.rcParams['font.sans-serif'][0]\n",
    "        if current_font == 'DejaVu Sans':\n",
    "            use_english = True\n",
    "            print(\"⚠️ 检测到中文字体可能显示异常，将使用英文标签\")\n",
    "    except:\n",
    "        use_english = True\n",
    "    \n",
    "    plt.close(test_fig)\n",
    "    \n",
    "    # 根据字体情况选择标题和标签\n",
    "    if use_english:\n",
    "        main_title = 'IP Address Count Analysis by Province'\n",
    "        chart_titles = [\n",
    "            'Top 15 Provinces by IP Count',\n",
    "            'Bottom 15 Provinces by IP Count', \n",
    "            'IP Count Distribution Histogram',\n",
    "            'Top 10 Provinces Percentage Analysis'\n",
    "        ]\n",
    "        axis_labels = {\n",
    "            'province': 'Province',\n",
    "            'count': 'IP Count',\n",
    "            'frequency': 'Frequency'\n",
    "        }\n",
    "        legend_labels = {\n",
    "            'mean': 'Mean',\n",
    "            'median': 'Median',\n",
    "            'others': 'Others'\n",
    "        }\n",
    "    else:\n",
    "        main_title = '各省份IP地址数量统计分析'\n",
    "        chart_titles = [\n",
    "            'IP地址数量前15名省份',\n",
    "            'IP地址数量后15名省份',\n",
    "            'IP地址数量分布直方图', \n",
    "            'IP地址数量占比分析'\n",
    "        ]\n",
    "        axis_labels = {\n",
    "            'province': '省份',\n",
    "            'count': 'IP地址数量',\n",
    "            'frequency': '省份数量'\n",
    "        }\n",
    "        legend_labels = {\n",
    "            'mean': '平均值',\n",
    "            'median': '中位数',\n",
    "            'others': '其他省份'\n",
    "        }\n",
    "    \n",
    "    # 创建图表布局\n",
    "    fig, axes = plt.subplots(2, 2, figsize=(18, 12))\n",
    "    fig.suptitle(main_title, fontsize=16, fontweight='bold')\n",
    "    \n",
    "    # 按计数排序数据\n",
    "    sorted_data = province_count_result.sort_values('count', ascending=False)\n",
    "    \n",
    "    # 图1: 前15名省份柱状图\n",
    "    ax1 = axes[0, 0]\n",
    "    top_15 = sorted_data.head(15)\n",
    "    bars1 = ax1.bar(range(len(top_15)), top_15['count'], \n",
    "                    color=plt.cm.plasma(np.linspace(0, 1, len(top_15))))\n",
    "    ax1.set_title(chart_titles[0], fontsize=12, fontweight='bold')\n",
    "    ax1.set_xlabel(axis_labels['province'], fontsize=10)\n",
    "    ax1.set_ylabel(axis_labels['count'], fontsize=10)\n",
    "    ax1.set_xticks(range(len(top_15)))\n",
    "    ax1.set_xticklabels(top_15['province'], rotation=45, ha='right')\n",
    "    \n",
    "    # 在柱子上显示数值\n",
    "    for i, (bar, value) in enumerate(zip(bars1, top_15['count'])):\n",
    "        height = bar.get_height()\n",
    "        ax1.text(bar.get_x() + bar.get_width()/2., height + max(top_15['count'])*0.01,\n",
    "                f'{value:,}', ha='center', va='bottom', fontsize=8)\n",
    "    \n",
    "    # 图2: 后15名省份柱状图\n",
    "    ax2 = axes[0, 1]\n",
    "    bottom_15 = sorted_data.tail(15)\n",
    "    bars2 = ax2.bar(range(len(bottom_15)), bottom_15['count'], \n",
    "                    color=plt.cm.cool(np.linspace(0, 1, len(bottom_15))))\n",
    "    ax2.set_title(chart_titles[1], fontsize=12, fontweight='bold')\n",
    "    ax2.set_xlabel(axis_labels['province'], fontsize=10)\n",
    "    ax2.set_ylabel(axis_labels['count'], fontsize=10)\n",
    "    ax2.set_xticks(range(len(bottom_15)))\n",
    "    ax2.set_xticklabels(bottom_15['province'], rotation=45, ha='right')\n",
    "    \n",
    "    # 在柱子上显示数值\n",
    "    for i, (bar, value) in enumerate(zip(bars2, bottom_15['count'])):\n",
    "        height = bar.get_height()\n",
    "        ax2.text(bar.get_x() + bar.get_width()/2., height + max(bottom_15['count'])*0.01,\n",
    "                f'{value:,}', ha='center', va='bottom', fontsize=8)\n",
    "    \n",
    "    # 图3: IP地址数量分布直方图\n",
    "    ax3 = axes[1, 0]\n",
    "    ax3.hist(province_count_result['count'], bins=20, color='lightblue', \n",
    "             alpha=0.7, edgecolor='black')\n",
    "    ax3.set_title(chart_titles[2], fontsize=12, fontweight='bold')\n",
    "    ax3.set_xlabel(axis_labels['count'], fontsize=10)\n",
    "    ax3.set_ylabel(axis_labels['frequency'], fontsize=10)\n",
    "    \n",
    "    # 添加统计线\n",
    "    mean_count = province_count_result['count'].mean()\n",
    "    median_count = province_count_result['count'].median()\n",
    "    ax3.axvline(mean_count, color='red', linestyle='--', linewidth=2, label=f'{legend_labels[\"mean\"]}: {mean_count:.0f}')\n",
    "    ax3.axvline(median_count, color='green', linestyle='--', linewidth=2, label=f'{legend_labels[\"median\"]}: {median_count:.0f}')\n",
    "    ax3.legend()\n",
    "    \n",
    "    # 图4: 饼图显示前10名省份占比\n",
    "    ax4 = axes[1, 1]\n",
    "    top_10 = sorted_data.head(10)\n",
    "    others_count = sorted_data.tail(len(sorted_data) - 10)['count'].sum() if len(sorted_data) > 10 else 0\n",
    "    \n",
    "    # 准备饼图数据\n",
    "    pie_data = list(top_10['count'])\n",
    "    pie_labels = list(top_10['province'])\n",
    "    if others_count > 0:\n",
    "        pie_data.append(others_count)\n",
    "        pie_labels.append(legend_labels['others'])\n",
    "    \n",
    "    # 生成颜色\n",
    "    colors = plt.cm.Set3(np.linspace(0, 1, len(pie_data)))\n",
    "    \n",
    "    wedges, texts, autotexts = ax4.pie(pie_data, labels=pie_labels, autopct='%1.1f%%',\n",
    "                                       startangle=90, colors=colors)\n",
    "    ax4.set_title(chart_titles[3], fontsize=12, fontweight='bold')\n",
    "    \n",
    "    # 美化饼图文本\n",
    "    for autotext in autotexts:\n",
    "        autotext.set_color('white')\n",
    "        autotext.set_fontweight('bold')\n",
    "        autotext.set_fontsize(8)\n",
    "    \n",
    "    # 调整布局\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # 显示图表说明\n",
    "    print(\"\\n📊 Chart Description:\" if use_english else \"\\n📊 图表说明:\")\n",
    "    if use_english:\n",
    "        print(\"• Top-left: Top 15 provinces with most IP addresses\")\n",
    "        print(\"• Top-right: Bottom 15 provinces with least IP addresses\") \n",
    "        print(\"• Bottom-left: Overall distribution of IP counts, red line=mean, green line=median\")\n",
    "        print(\"• Bottom-right: Percentage analysis of top 10 provinces\")\n",
    "    else:\n",
    "        print(\"• 左上图: 显示IP地址数量最多的前15个省份\")\n",
    "        print(\"• 右上图: 显示IP地址数量最少的后15个省份\") \n",
    "        print(\"• 左下图: IP地址数量的整体分布情况，红线为平均值，绿线为中位数\")\n",
    "        print(\"• 右下图: 前10名省份的IP地址数量占比分析\")\n",
    "    \n",
    "    # 输出关键洞察\n",
    "    total_ip = province_count_result['count'].sum()\n",
    "    top_5_total = sorted_data.head(5)['count'].sum()\n",
    "    top_5_percent = (top_5_total / total_ip) * 100\n",
    "    \n",
    "    print(f\"\\n🔍 Key Insights:\" if use_english else f\"\\n🔍 关键洞察:\")\n",
    "    if use_english:\n",
    "        print(f\"• Top 5 provinces account for {top_5_percent:.1f}% of total IP addresses\")\n",
    "        print(f\"• Province with most IPs: {sorted_data.iloc[0]['province']} ({sorted_data.iloc[0]['count']:,} IPs)\")\n",
    "        print(f\"• Province with least IPs: {sorted_data.iloc[-1]['province']} ({sorted_data.iloc[-1]['count']:,} IPs)\")\n",
    "        print(f\"• IP count difference ratio: {sorted_data.iloc[0]['count'] / sorted_data.iloc[-1]['count']:.0f}x\")\n",
    "    else:\n",
    "        print(f\"• 前5名省份占总IP地址数量的 {top_5_percent:.1f}%\")\n",
    "        print(f\"• 最多的省份({sorted_data.iloc[0]['province']})有 {sorted_data.iloc[0]['count']:,} 个IP地址\")\n",
    "        print(f\"• 最少的省份({sorted_data.iloc[-1]['province']})有 {sorted_data.iloc[-1]['count']:,} 个IP地址\")\n",
    "        print(f\"• 省份间IP地址数量差异达 {sorted_data.iloc[0]['count'] / sorted_data.iloc[-1]['count']:.0f} 倍\")\n",
    "\n",
    "# 创建柱状图\n",
    "print(\"🎨 创建省份IP地址统计柱状图...\")\n",
    "create_province_count_charts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "57b1ac1a-dcb6-4427-ba93-66dce8aeddb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎨 创建四个独立的图表...\n",
      "⚠️ 中文字体可能异常，使用英文标签\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🔍 Key Insights:\n",
      "• Top 5 provinces account for 33.0% of total IP addresses\n",
      "• Province with most IPs: 广东省 (6,192 IPs)\n",
      "• Province with least IPs: 贵州 (1 IPs)\n",
      "• IP count difference ratio: 6192x\n"
     ]
    }
   ],
   "source": [
    "#张凯作业\n",
    "# 创建四个独立的可视化图表\n",
    "def create_separate_charts():\n",
    "    \"\"\"创建四个独立的图表：前15、后15、折线图、饼图\"\"\"\n",
    "    if 'province_count_result' not in globals() or province_count_result is None or province_count_result.empty:\n",
    "        print(\"⚠️ 没有省份统计数据可供可视化\")\n",
    "        return\n",
    "\n",
    "    # 设置图表样式\n",
    "    plt.style.use('default')\n",
    "    sns.set_palette(\"viridis\")\n",
    "\n",
    "    # 检测中文字体是否正常工作\n",
    "    test_fig, test_ax = plt.subplots(figsize=(1, 1))\n",
    "    test_ax.text(0.5, 0.5, '测试中文', fontsize=12)\n",
    "    test_fig.canvas.draw()\n",
    "\n",
    "    use_english = False\n",
    "    try:\n",
    "        current_font = plt.rcParams['font.sans-serif'][0]\n",
    "        if current_font == 'DejaVu Sans':\n",
    "            use_english = True\n",
    "            print(\"⚠️ 中文字体可能异常，使用英文标签\")\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    plt.close(test_fig)\n",
    "\n",
    "    # 根据字体情况选择标题和标签\n",
    "    if use_english:\n",
    "        chart_titles = [\n",
    "            'Top 15 Provinces by IP Count',\n",
    "            'Bottom 15 Provinces by IP Count',\n",
    "            'IP Count Distribution Trend (Line Chart)',\n",
    "            'Top 10 Provinces Percentage Analysis'\n",
    "        ]\n",
    "        axis_labels = {\n",
    "            'province': 'Province',\n",
    "            'count': 'IP Count'\n",
    "        }\n",
    "        legend_labels = {\n",
    "            'others': 'Others'\n",
    "        }\n",
    "    else:\n",
    "        chart_titles = [\n",
    "            'IP地址数量前15名省份',\n",
    "            'IP地址数量后15名省份',\n",
    "            'IP地址数量分布趋势（折线图）',\n",
    "            'IP地址数量占比分析'\n",
    "        ]\n",
    "        axis_labels = {\n",
    "            'province': '省份',\n",
    "            'count': 'IP地址数量'\n",
    "        }\n",
    "        legend_labels = {\n",
    "            'others': '其他省份'\n",
    "        }\n",
    "\n",
    "    sorted_data = province_count_result.sort_values('count', ascending=False)\n",
    "\n",
    "    # 图1: 前15名省份柱状图\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    top_15 = sorted_data.head(15)\n",
    "    bars = plt.bar(top_15['province'], top_15['count'],\n",
    "                   color=plt.cm.plasma(np.linspace(0, 1, len(top_15))))\n",
    "    plt.title(chart_titles[0], fontsize=14, fontweight='bold')\n",
    "    plt.xlabel(axis_labels['province'])\n",
    "    plt.ylabel(axis_labels['count'])\n",
    "    plt.xticks(rotation=45, ha='right')\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # 添加数值标签\n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        plt.text(bar.get_x() + bar.get_width()/2., height + max(top_15['count'])*0.01,\n",
    "                 f'{int(height):,}', ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    # 图2: 后15名省份柱状图\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    bottom_15 = sorted_data.tail(15)\n",
    "    bars = plt.bar(bottom_15['province'], bottom_15['count'],\n",
    "                   color=plt.cm.cool(np.linspace(0, 1, len(bottom_15))))\n",
    "    plt.title(chart_titles[1], fontsize=14, fontweight='bold')\n",
    "    plt.xlabel(axis_labels['province'])\n",
    "    plt.ylabel(axis_labels['count'])\n",
    "    plt.xticks(rotation=45, ha='right')\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # 添加数值标签\n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        plt.text(bar.get_x() + bar.get_width()/2., height + max(bottom_15['count'])*0.01,\n",
    "                 f'{int(height):,}', ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    # 图3: 折线图显示所有省份IP数量分布趋势\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.plot(sorted_data['province'], sorted_data['count'], marker='o', linestyle='-', color='teal')\n",
    "    plt.title(chart_titles[2], fontsize=14, fontweight='bold')\n",
    "    plt.xlabel(axis_labels['province'])\n",
    "    plt.ylabel(axis_labels['count'])\n",
    "    plt.xticks(rotation=90, fontsize=8)\n",
    "    plt.grid(True, linestyle='--', alpha=0.5)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # 图4: 饼图显示前10名省份占比\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    top_10 = sorted_data.head(10)\n",
    "    others_count = sorted_data.tail(len(sorted_data) - 10)['count'].sum() if len(sorted_data) > 10 else 0\n",
    "\n",
    "    pie_data = list(top_10['count'])\n",
    "    pie_labels = list(top_10['province'])\n",
    "    if others_count > 0:\n",
    "        pie_data.append(others_count)\n",
    "        pie_labels.append(legend_labels['others'])\n",
    "\n",
    "    colors = plt.cm.Set3(np.linspace(0, 1, len(pie_data)))\n",
    "\n",
    "    wedges, texts, autotexts = plt.pie(pie_data, labels=pie_labels, autopct='%1.1f%%',\n",
    "                                      startangle=90, colors=colors, textprops={'fontsize': 10})\n",
    "    plt.title(chart_titles[3], fontsize=14, fontweight='bold')\n",
    "\n",
    "    for autotext in autotexts:\n",
    "        autotext.set_color('white')\n",
    "        autotext.set_fontweight('bold')\n",
    "        autotext.set_fontsize(10)\n",
    "\n",
    "    plt.axis('equal')  # 保证饼图为圆形\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # 输出关键洞察\n",
    "    total_ip = province_count_result['count'].sum()\n",
    "    top_5_total = sorted_data.head(5)['count'].sum()\n",
    "    top_5_percent = (top_5_total / total_ip) * 100\n",
    "\n",
    "    print(f\"\\n🔍 Key Insights:\" if use_english else f\"\\n🔍 关键洞察:\")\n",
    "    if use_english:\n",
    "        print(f\"• Top 5 provinces account for {top_5_percent:.1f}% of total IP addresses\")\n",
    "        print(f\"• Province with most IPs: {sorted_data.iloc[0]['province']} ({sorted_data.iloc[0]['count']:,} IPs)\")\n",
    "        print(f\"• Province with least IPs: {sorted_data.iloc[-1]['province']} ({sorted_data.iloc[-1]['count']:,} IPs)\")\n",
    "        print(f\"• IP count difference ratio: {sorted_data.iloc[0]['count'] / sorted_data.iloc[-1]['count']:.0f}x\")\n",
    "    else:\n",
    "        print(f\"• 前5名省份占总IP地址数量的 {top_5_percent:.1f}%\")\n",
    "        print(f\"• 最多的省份({sorted_data.iloc[0]['province']})有 {sorted_data.iloc[0]['count']:,} 个IP地址\")\n",
    "        print(f\"• 最少的省份({sorted_data.iloc[-1]['province']})有 {sorted_data.iloc[-1]['count']:,} 个IP地址\")\n",
    "        print(f\"• 省份间IP地址数量差异达 {sorted_data.iloc[0]['count'] / sorted_data.iloc[-1]['count']:.0f} 倍\")\n",
    "\n",
    "\n",
    "# 替换原来的 create_province_count_charts() 调用\n",
    "print(\"🎨 创建四个独立的图表...\")\n",
    "create_separate_charts()"
   ]
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